# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Thresholded Rectified Linear Unit activation layer."""
# pylint: disable=g-classes-have-attributes,g-direct-tensorflow-import

from keras import backend
from keras.engine.base_layer import Layer
from keras.utils import tf_utils
import tensorflow.compat.v2 as tf

from tensorflow.python.util.tf_export import keras_export


@keras_export('keras.layers.ThresholdedReLU')
class ThresholdedReLU(Layer):
  """Thresholded Rectified Linear Unit.

  It follows:

  ```
    f(x) = x for x > theta
    f(x) = 0 otherwise`
  ```

  Input shape:
    Arbitrary. Use the keyword argument `input_shape`
    (tuple of integers, does not include the samples axis)
    when using this layer as the first layer in a model.

  Output shape:
    Same shape as the input.

  Args:
    theta: Float >= 0. Threshold location of activation.
  """

  def __init__(self, theta=1.0, **kwargs):
    super(ThresholdedReLU, self).__init__(**kwargs)
    if theta is None:
      raise ValueError(
          'Theta of a Thresholded ReLU layer cannot be None, expecting a float.'
          f' Received: {theta}')
    if theta < 0:
      raise ValueError('The theta value of a Thresholded ReLU layer '
                       f'should be >=0. Received: {theta}')
    self.supports_masking = True
    self.theta = backend.cast_to_floatx(theta)

  def call(self, inputs):
    theta = tf.cast(self.theta, inputs.dtype)
    return inputs * tf.cast(tf.greater(inputs, theta), inputs.dtype)

  def get_config(self):
    config = {'theta': float(self.theta)}
    base_config = super(ThresholdedReLU, self).get_config()
    return dict(list(base_config.items()) + list(config.items()))

  @tf_utils.shape_type_conversion
  def compute_output_shape(self, input_shape):
    return input_shape
